International Scientific Symposium on Logistics 2021

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2021

International Scientific Symposium on Logistics

June 15, 2021

Conference Volume

Digital Event

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2021

International Scientific Symposium on Logistics

Conference Volume

edited by Thorsten Schmidt

Kai Furmans Michael Freitag Bernd Hellingrath

René de Koster

Anne Lange

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Imprint Published by:

Bundesvereinigung Logistik (BVL) e.V.

Schlachte 31 28195 Bremen

Phone: +49 (0)421 17 38 40 Fax: +49 (0)421 16 78 00 Mail: bvl@bvl.de

Internet: www.bvl.de

Editors: Thorsten Schmidt, Kai Furmans,

Michael Freitag, Bernd Hellingrath, René de Koster, Anne Lange Editorial support: Frank Schulze, Oliver Schubert

This publication is protected by copyright. No part of this publication may be reproduced without the consent of the publisher. This applies in particular to duplications, translations and micro- filming as well as storage and processing in electronic media. However, the publisher grants the authors the right to use and re-publish their own contributions according to Creative Commons license CC BY-SA 4.0.

Despite painstaking research, it is not always possible to ascertain the authors of the images. If images have been unintentionally published in a manner that is not desired, please inform the publisher accordingly.

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The 2021 International Scientific Symposium on Logistics is a joint event of Bundesvereinigung Logistik

and Fraunhofer Institute for Material Flow and Logistics.

Preface of the Editors

Thorsten Schmidt, Institute of Material Handling and Industrial Engineering, Technische Universität Dresden

Kai Furmans, Institute for Material Handling and Logistics Karlsruhe Institute of Technology

Dear researchers in logistics,

the pandemic influences our life to an extent, which we knew from disaster movies only. Yet, in the meantime we somehow managed to adjust our professional life and working habits to these circumstances. The (advanced) mastering of any video meeting tool is part of that, but merely worth mentioning. In our distinct communities we moved “into virtuality” and often perform surprisingly efficient.

However, there is one element that certainly was not cultivated in its proper form. This is the cross view into other logistical disciplines, the interaction with the fields just outside of our core expertises. The International Scientific Symposium in Logistics (ISSL – in 2021 meanwhile the 10^th) stands from its beginning for exactly this: a scientific meeting covering the entire spectrum in logistics in its broadest form.

The contributions to the 2021 ISSL address the current challenges to logistics from various perspectives and deliver a valuable contribution to any logistics scientist with a unique look at the interaction between economic aspects, technology and humans and how these impact on the shape of tomorrow’s supply chains.

Dresden, in May 2021 Thorsten Schmidt Kai Furmans

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Preface of Bundesvereinigung Logistik (BVL)

Christoph Meyer, Managing Director, Bundesvereinigung Logistik,

Susanne Grosskopf-Nehls, Senior Project Manager, Bundesvereinigung Logistik

Dear participants of this year’s ISSL, Dear readers of this congress volume,

Under the title “Logistics for a Sustainable Future – Contributions from Science”, the Inter- national Scientific Symposium on Logistics (ISSL) was originally scheduled to take place in Dresden in June 2020. It would have been the tenth symposium. Preparations for the content had already begun in 2019. The program committee includes Professors Kai Furmans, Michael Freitag, Bernd Hellingrath, René de Koster, Anne Lange and Thorsten Schmidt.

Now we are one year on, the Corona pandemic has changed the world. In 2020, the ISSL finally had to be cancelled due to the pandemic. But the logistics science event is alive and well – and the “sustainability” theme chosen for 2020 still strikes a chord. Thus, all those who assumed that this was just a short-term “hype topic” were wrong. Moreover, logistics experts have been dealing with the topic for a much longer time already: Green Logistics had already been dis- cussed scientifically in the BVL environment ten years ago. In terms of society as a whole and the economy as a whole, various factors have contributed to an intensification of the discourse.

Consequently, the ISSL program committee had the submissions updated. In light of digital-only implementation, the program has been streamlined.

On June 15, 2021, participants will be able to join this “anniversary” and first-ever all-digital ISSL with just one click. Researchers and practitioners alike will find inspiration, insights, and knowledge – real “take away value”: three keynotes and 18 technical presentations in 6 sessions, as well as reading this congress volume, offer first-hand information and knowledge. Sincere thanks are due to all contributors and to the members of the program committee who also edited this volume.

The symposium was made possible by the joint efforts of TU Dresden, Fraunhofer IML in Dortmund and BVL. We would like to thank all participants as well as the members of the Scientific Advisory Board of BVL for their great commitment and perseverance in continuing the event series against all odds. The BVL wishes all participants of the ISSL 2021 and all readers of the congress proceedings lasting inspiration and impulses.

Bremen, in June 2021 Christoph Meyer

Susanne Grosskopf-Nehls

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Solving Sustainability Problems:

Lessons Learned in Transport and Logistics

Dirk Helbing, Computational Social Science, ETH Zürich, Switzerland

Keynote

Summary. Our economy and the underlying supply chains are complex dynamical systems, which may show features of self-organization and emergence. As a consequence, disruptions as well as control attempts, can have unexpected side effects, feedback effects and cascading effects. Instabilities, as reflected by bull-whip effects and business cycles, are common as well.

The question is, therefore, how these flows can be organized efficiently.

In case of conflicting traffic flows, e.g. at intersections of an urban road network, centralized optimization and synchronized cyclical control have been common approaches over many decades. In this talk, however, I will present a new approach called "self-control", which is based on a self-organization of the flows in the network based on short-term anticipation and local coordination. This turns out to be a superior solution approach. I will argue that these principles can be used to better distribute perishable goods such as food, and introduce a socio-ecological finance system, called Finance 4.0, which can support the co-evolution towards a more sustainable circular and sharing economy.

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Behavioral Issues in Automated Warehouses:

Unifying Framework and Research Agenda

Alexander Hübner, Supply & Value Chain Management, Technical University of Munich Fabian Lorson, Supply & Value Chain Management, Technical University of Munich Andreas Fügener, Digital Supply Chain Management, University of Cologne

Extended Abstract

Summary. We identify and analyze relevant behavioral issues of human interactions with automated and robotized warehousing systems for operational activities. By developing and applying a unifying framework, we structurally discuss interaction setup, targeted operational activity, associated human factors and behavior, as well as the impact on system performance.

Using expert interviews with practitioners, we identify the most relevant interactions and behavioral issues, while a structured literature review allows us to develop future research questions.

1. Introduction and Motivation

Over decades, warehouse operations have relied on manual processes as human operators had been more efficient in many aspects, such as picking a large variety of products. Enabled by advances in Internet of Things devices and artificial intelligence coupled with the advent of new system providers and more cost-efficient solutions, warehousing has been revolutionized during the last decade: Human operators found themselves next to new robotized and automated teammates (Olsen and Tomlin, 2020). The size of the warehouse automation industry has been growing by 12% annually between 2014 and 2019, and is predicted to double its size from USD 15 billion to USD 30 billion in the next six years (IFR, 2020; Statista, 2020; The Logistics iQ, 2020). The resulting development and utilization of novel automated and robotized systems are boosting the transformation of warehousing from a cost center to a central component in the value proposition of firms. For instance, Amazon is currently employing more than 200,000 warehouse robots to accelerate its growth in online retail and logistics (IHCI, 2020). There are many other examples which show that innovations in warehouse automation play a crucial part in delivering products efficiently and effectively throughout supply chains (Swisslog, 2020).

Despite the growing and ubiquitous presence of automated and robotized systems, manual labor will be needed simultaneously with such machines in many warehouses in the future due to distinctive human capabilities and economic advantages. To manage resulting human-machine

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Hübner, Lorson, and Fügener

interactions efficiently, new frameworks and concepts are needed (Olsen and Tomlin, 2020). As human actions and decisions in such interactions may deviate from traditional assumptions and thus impact operations management metrics in both positive and negative directions (Boudreau et al., 2003; Bendoly et al., 2006; Croson et al., 2013), it is imperative to account for human factors of workers in operational activities, and to consider behavioral methodologies since they provide the opportunity to resolve emerging issues in human-machine interactions (Kumar et al., 2018). Hence, this is the first paper that structurally analyzes human-machine interactions in the warehouse by building and applying a unifying framework. Additionally, we identify the most relevant behavioral issues for these interactions. Ultimately, we establish a research agenda to improve operational decision-making for human interactions with automated and robotized systems.

2. Research Methodology

We want to generate a holistic and accurate understanding for the emerging research area of human-machine interactions in warehousing, while we need to cope with the scarcity of existing contributions. Multi-method approaches are imperative in such cases (see Boyer and Swink (2008); DeHoratius and Rabinovich (2011); Flick et al. (2004); Singhal et al. (2008) for examples). We follow well-established guidelines for emerging topics (Webster and Watson, 2002) and first develop the theoretical foundation by analyzing seminal literature in operations management of warehouses, behavioral science and human-machine interaction theory.

Secondly, we conduct semi-structured expert interviews to identify the most relevant human- machine interactions and capture the associated behavioral issues as recommended by Edmondson and Mcmanus (2007). Finally, we perform a systematic literature analysis for the issues identified. This is a pivotal step to deepen links among managerial relevant issues and existing work, and necessary to identify current gaps in literature (DeHoratius and Rabinovich, 2011). The theoretical foundation is the input to develop the unifying, conceptual framework (see Section 3). The expert interviews are conducted to identify the most relevant interactions and corresponding behavioral issues, while the systematic literature analysis allows us to develop resulting open research questions. We discuss each behavioral issue along our unifying framework, ensuring a structured approach to investigate and enrich the issue analysis (see Section 4). In this way, we provide insights by complementing and characterizing the behavioral issues with theory on associated human factors and potential impact on system performance. To enhance the confidence in our findings, only the continuous and comprehensive triangulation of all these sources provides the opportunity to validate the proposed framework, systematically identify and analyze relevant issues, and ultimately create a comprehensive research agenda.

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Conference Volume 9

3. Unifying Framework for Human-Machine Interactions in Warehouses

Figure 1. Unifying framework to discuss behavioral issues of human-machine interactions

The framework developed links human-machine interactions with the respective operational warehouse activities and human factors and behavior, and elaborates consequences on system performance. This serves to identify a set of potential interactions and issues that may exist. We utilized theories from related fields to rely on existing definitions and relationships for our framework (Ramasesh and Browning, 2014). Using our framework allows us to structurally discuss the following identified issues.

4. Relevant Behavioral Issues of Human-Machine Interactions

Figure 2. Overview of the seven issues identified along operational activity and interaction setup

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Issue identification and description. One key challenge revealed in our expert interviews is determining the team structure, that is, how many humans and how many autonomous picking robots to employ for a given picking zone during the same shift: “I will have to form new teams, and this will change the human dynamics significantly depending on how many robots I will include,” to cite a warehouse operator. In mixed teams, humans will see the autonomous robots, hear their noises, and maybe even smell their robotic odor. Humans may think about robots as team mates, their role within the team, and how to deal with them. They need to move around them to both ensure human safety and robot productivity. Many experts have also reported different ways employees have of coping with such close human-machine coexistence, with one warehouse manager pointing to unknown consequences: “We do not know yet what the short- and long-term influence on human social components will be when we employ more and more robots.”

Implications on human behavior. The physical human factors mentioned influence psychosocial factors and behavior in many ways. The perception of a robot working at a different speed might alter the human motivation to work efficiently, the human may have to cope with the fact that a robot has replaced their human team mate, or the human may experience a lack of team spirit.

Outside the warehousing literature, a plethora of theories exist on behavioral issues regarding team composition in general and for human-machine interactions in particular. One key aspect of managing teams is to deal with interpersonal processes such as conflict and affect management or collective motivation building to avoid performance problems (Marks et al., 2001). Employees care about human relationships and identify with colleagues (Urda and Loch, 2013), and these social interactions have a large impact on motivation and performance (Cantor and Jin, 2019). In line with that, Stein and Scholz (2019) encourage automation-oriented diversity management when building groups and Gombolay et al. (2015) establish that people value humans more than robots as team members. Hence, psychosocial factors such as motivation, job satisfaction or loyalty of employees may vary depending on the team structure in warehouse operations, too. Additionally, findings about peer effects (Tan and Netessine, 2019) may also exist for such human-machine teaming and impact optimal operating policies. The physical presence of autonomous picking robots may further influence trust and actions, depending on the individual human being (Glikson and Woolley, 2020). Consequently, this requires a thorough understanding of which personalities, behavioral traits or skills prove to enhance performance criteria.

Related literature and gap. As humans and robots have formed teams only recently for picking activities, this constitutes a new area of research including the following questions:

 How does the share of robots impact the efficiency and retention of human operators, why does it differ (e.g., human-robot peer effects, individual motivation and trust), and what is the optimal share and policy in which constellations?

 Which behavioral traits and skills impact performance when teaming with autonomous robots, what behavioral aspects may explain differences (e.g., satisfaction, stress), and why?

5. Conclusion

Interactions among human operators and automated or robotized systems in the warehouse are developing into a multi-disciplinary field of research, and has recently evolved and gained momentum due to the rapid growth of automation in logistics. This raises new issues related to the role of workers in warehousing and in operations of the future. As such, it has become

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essential to investigate and optimize human-machine interactions in operational warehouse activities. This paper develops the pathway to upcoming research within this context by identifying key human-machine interactions and corresponding behavioral issues. We first contributed to theory building and developed a unifying framework to structurally analyze issues in such interactions to tackle this nascent research area. We presented our empirical findings from expert discussions and combined those with relevant behavioral theory and existing warehousing literature that revealed significant gaps. The research agenda developed unfolds interesting and relevant research propositions.

The managerial implications of this paper contribute to design aspects for warehouse systems providers, the decision-making processes of warehouse managers, and the awareness for project managers on behavioral issues in warehouse automation projects. The unifying framework can additionally applied in other contexts, particularly towards human-machine interactions of both different activity levels and related operations management fields.

Future interdisciplinary research can leverage the findings to apply a variety of methods to address the research questions proposed, and to inform operations management models, theories and principles.

References

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Boudreau, J., Hopp, W., McClain, J. O., Thomas, L. J., 2003. On the Interface Between Operations and Human Resources Management. Manufacturing & Service Operations Management 5 (3), 179–202.

Boyer, K. K., Swink, M. L., 2008. Empirical Elephants-Why Multiple Methods are Essential to Quality Research in Operations and Supply Chain Management. Journal of Operations Management 26 (3), 338–344.

Cantor, D. E., Jin, Y., 2019. Theoretical and empirical evidence of behavioral and production line factors that influence helping behavior. Journal of Operations Management 65 (4), 312–

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Croson, R., Schultz, K., Siemsen, E., Yeo, M. L., 2013. Behavioral operations: The state of the field. Journal of Operations Management 31 (1-2), 1–5.

DeHoratius, N., Rabinovich, E., 2011. Field research in operations and supply chain management. Journal of Operations Management 29 (5), 371–375.

Edmondson, A. C., Mcmanus, S. E., 2007. Methodological fit in management field research.

Academy of Management Review 32 (4), 1246–1264.

Flick, U., von Kardorff, E., Steinke, I. (Eds.), 2004. A Companion to Qualitative Research. Sage Publications, London.

Glikson, E., Woolley, A. W., 2020. Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals 14 (2), 627–660.

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Gombolay, M., Bair, A., Huang, C., Shah, J., 2017. Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences. The International Journal of Robotics Research 36 (5-7), 597–617.

IFR, 2020. Executive Summary World Robotics 2020 Industrial Robots. http://ifr.org/img/

worldrobotics/Executive_Summary_WR_2020_Industrial_Robots_1.pdf

IHCI, 2020. Amazon has >200,000 Kiva robots navigating through its warehouses across the globe. Assessed on 19.04.2021. https://ihci.sbf.org.sg/docs/default-source/application- guides/ag28-rfw/app-guide-28_case-1.pdf?sfvrsn=8e119260_2

Kumar, S., Mookerjee, V., Shubham, A., 2018. Research in Operations Management and Information Systems Interface. Production and Operations Management 27 (11), 1893–

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Marks, M. A., Mathieu, J. E., Zaccaro, S. J., 2001. A Temporally Based Framework and Taxonomy of Team Processes. Academy of Management Review 26 (3), 356–376.

Olsen, T. L., Tomlin, B., 2020. Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management 22 (1), 113–122.

Ramasesh, R. V., Browning, T. R., 2014. A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management 32 (4), 190–204.

Singhal, V., Flynn, B. B., Ward, P. T., Roth, A. V., Gaur, V., 2008. Editorial: Empirical elephants- Why multiple methods are essential to quality research in operations and supply chain management. Journal of Operations Management 26 (3), 345–348.

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https://www.statista.com/statistics/1094202/global-warehouse-automation-market-size/

Stein, V., Scholz, T. M., 2019. Manufacturing Revolution Boosts People Issues: The Evolutionary Need for ’Human–Automation Resource Management’ in Smart Factories. European Management Review.

Swisslog, 2020. Swisslog congratulates dm on the German Logistics Award 2020. Assessed on 09.02.2021. https://www.swisslog.com/en-us/newsroom/news-press-releases-blog-posts /2020/10/swisslog-dm-drogerie-markt-german-logistics-prize

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Method for the Evaluation of

An Autonomous Handling System

For Improving the Process Efficiency of Container Unloading

Jasper Wilhelm, Research Associate,

BIBA - Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Germany Nils Hendrik Hoppe, Research Associate,

Faculty of Production Engineering, University of Bremen, Germany Paul Kreuzer, Senior Developer,

Framos GmbH, Taufkirchen, Germany Christoph Petzoldt, Department Head,

BIBA - Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Germany Lennart Rolfs, Research Associate,

BIBA - Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Germany Michael Freitag, Director,

BIBA - Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Germany, and Professor, Faculty of Production Engineering, University of Bremen, Germany

Summary. Rising trade volume creates an increasing need for automatic unloading solutions for containers. Some systems are already on the market but not widely used due to lack of robustness and difficult-to-predict performance. We present the first approach towards a uni- versal estimation of unloading performance and apply it to a new system. We divide the unloading process into five steps, made up of six individual tasks, and present the ten parameters affecting these tasks. We show how the total unloading time and performance can be calculated based on the task times, reducing the number of necessary tests. Using this method, we calculate the unloading performance of a system gripping multiple cartons. The estimated performance ranges from 341 to 3,252 cartons per hour. This shows that for many systems, the unloading performance depends on multiple parameters. We anticipate this contribution to serve as the first step towards a standardized calculation of unloading performance for containers.

1. Introduction

International trade volume is steadily rising (World Trade Organization 2020, 12). The majority of this cargo is transported in containers, mainly by ship, and packed and emptied in the hinter- land (United Nations 2020, 9). To increase transport efficiency, both on ship and truck, cargo in

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Wilhelm, Hoppe et al.

containers are loaded with individual cartons instead of pallets, complicating the emptying of containers (Bortfeldt and Wäscher 2013; Zhao et al. 2016).

Several automatic handling solutions for unloading containers are available on the market but are not economical, as fully autonomous solutions often fail due to the varying loading patterns (Wilhelm, Beinke, and Freitag 2020). Existing solutions either pick items individually, limiting the potential throughput, or handle cartons in bulk, potentially damaging fragile goods. In this contribution, we present an overview of available systems for container unloading and provide a first evaluate of the unloading performance of a newly developed solution for the container unloading. We propose a process segmentation of unloading-tasks for an objective calculation of unloading performance and robustness in different scenarios. We evaluate this method on a newly developed system for the autonomous unloading of containers.

The remainder of this work is organized as follows. Section 2 reviews currently available container-unloading systems and describes a semi-autonomous system recently developed by the authors. The method for evaluating the unloading system and its performance is presented in Section 3. In Section 4 we describe the test-bed and experiments and present the resulting task time and unloading performance. Section 5 concludes this article and presents both future work and further perspectives.

2. State of the Art and System Description

2.1 State of the Art

Recent overviews of autonomous unloading systems for stacked cartons in containers are presented in Wilhelm, Beinke, and Freitag (2020) and Freitag et al. (2020). Despite the large number of unloading systems for containers or trucks, none of these solutions have achieved widespread use yet (Wilhelm, Beinke, and Freitag 2020). Reasons are the high variability of packing patterns and short process times, which is currently hard to achieve for fully autonomous systems (Petzoldt et al. 2020). Especially in complex scenarios this leads to system downtimes and costly manual interventions (Freitag et al. 2020).

The available solutions can be classified by various characteristics (Petzoldt et al. 2020; Freitag et al. 2020). One feature that directly affects the process time is the type of unloading. Tech- niques are the individual picking of items (Boston Dynamics 2021; Bastian Solutions 2018;

Stoyanov et al. 2016), gripping of multiple cartons stored in a row (Honeywell Intelligrated 2019), and bulk-unloading of the entire content of the container on conveyor belts (Siemens Logistics GmbH 2019; Honeywell Intelligrated 2019). In the bulk-unloading scenario, the system does not pick up individual items, but unloads the entire cargo of the container via conveyor technology in the floor, potentially damaging the cartons due to falls. The unloading speed ranges from 500 (Echelmeyer, Bonini, and Rohde 2014) to 1,000 cartons per hour (Bastian Solutions 2018) for individually picked items to 25,000 cartons per hour in the bulk unloading scenario (Siemens Logistics GmbH 2019). The unloading performance of human operators ranges from 420 to 840 cartons per hour (Petzoldt et al. 2020). The unloading speed for all systems presented are extracted from commercial publications and therefore not reviewed. An independent evaluation or methods for calculating the unloading speeds are not available.

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Method for the Evaluation of an Autonomous Handling System

Unloading Type Unloading speed in cartons/h Manual 420 .. 840 Petzoldt et al. 2020 Individual picking 500

1,000

Echelmeyer, Bonini, and Rohde 2014;

Bastian Solutions 2018 Multi-grip 1,500 ^*Krantz 2021

Bulk 1,500

25,000

* Krantz 2021;

Siemens Logistics GmbH 2019 Table 1. Performance of different unloading methods

* values given as the upper bound for a system that can perform both multi-grip and bulk-unloading

2.2 System for Multi-Grip Unloading

The method for the calculation of unloading times will be evaluated on a newly developed system for unloading loose-loaded containers in which the cargo is stacked in multiple layers (Petzoldt et al. 2020). To improve the throughput over unloading systems that pick items one at a time, the authors proposed a solution the unloading multiple items packed in a row (Petzoldt et al.

2020). This increases the unloading performance without high impact-loads on the items as in the bulk-unloading scenario.

The systems consists of a omnidirectional mobile chassis, a vertically moveable platform with tilt-adjustment, and three individually movable gripping-modules with vacuum suction cups to grip and pull cartons. The platform and center of the robot are equipped with conveyors to move the unloaded items to external material-handling technology at the back of the system. Figure 1 highlights the controllable parts of the system.

Figure 1. Unloading system with its degrees of freedom modules (in accordance with Petzoldt et al. 2020)

The system is equipped with an array of four RGB-D cameras, placed at the top and bottom, left and right corners of the vehicle. To increase robustness, the detection process runs independently on the four individual camera frames. After the detection of the box corners in the 3D color-frames, the positions of the corners are transformed into the common coordinate system of the robot. For carton identification we adapted a methodology based on deep neural networks (DNNs) which has been very successful in 2D Multi-Person Skeleton Estimation (Gong et al. 2016; Chen, Tian, and He 2020; Xiao, Wu, and Wei 2018; Cao et al. 2021). In this scenario,

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the four front-facing corners of the cartons present the skeleton to be detected. The key advantage of this concept over traditional computer vision methods is the long-term sustainability. This approach allows for a quick re-training in case the type of cargo changes, compared to traditional methods, in which an expert would have to adjust or even re-develop the algorithms.

2.3 Unloading Process

The unloading process performed by the robot consists of multiple steps, each built from unique tasks (Hoppe et al. 2020). Table 2 presents the list of process steps and their corresponding tasks. First, an array of four depth-cameras scans the area in front of the robot. A carton detection algorithm identifies the individual cartons in each of these four images and creates a skeleton representation of all identified objects. After merging this array of four skeletal images, the cartons for the next grip are chosen based on their reachability and the optimal unloading pose of the robot is calculated. Second, the robot approaches the cartons by moving its chassis and platform concurrently. To grab the cartons, the robot moves the gripping-modules to the front of the platform and starts the vacuum once the carton-front is in proximity of the suction cups. The vacuum is individually monitored and controlled so that the robot can distinguish between failed and successful grips. Once either all packages have been successfully grabbed or non-gripping suction cups are deactivated, the gripping-modules move to the rear. The robot switches off the vacuum and moves the gripper modules to their rest position, with the center module sinking below the conveyor belts. Finally, the conveyor modules unload all cartons pulled onto the platform.

𝑠 Step Tasks (𝑇) Conc.⁺

1 Identify Carton detection (1), Carton selection^*, Pose calculation^* 2 Approach Chassis motion (2), Platform motion (3) yes 3 Grab Gripper motion (4), Vacuum effect (5)

4 Pull Gripper motion (4), Vacuum effect (5) 5 Convey Conveyor motion (6)

Table 2. Individual tasks of the system to be tested based on the unloading process

* task time and robustness independent of parameters, negligible impact on unloading time

+ Tasks in this step are performed concurrently

3. Method

3.1 Experimental Design

Before the performance of the system is determined under real conditions in a field-test, integration and laboratory tests are performed to provide an initial estimate. We place particular emphasis on the coverage of as many potential scenarios as possible and the determination of the robustness of the solution.

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To minimize testing and create universally valid results, we propose a modular design of experiments. The separation of the overall process into individual steps and their basic tasks allows to specifically test each task 𝑇 with a reduced number of parameters. For each step 𝑠 only the relevant parameters of the tasks need to be adapted. The unloading time 𝑡_𝑟 for one row of cartons is the sum of the times 𝑡_𝑠 of the different steps based on the individual parameters 𝑝 of their underlying tasks 𝑇. In first approximation, each step is performed once for each row, since we assume that all cartons of a row are unloaded at once.

𝑡𝑟(𝑝₁, … , 𝑝𝑛) = ∑_{𝑠 ∈ 𝑆}𝑡𝑠(𝑝₁, … , 𝑝𝑛) (1) with

𝑡_𝑠(𝑝₁, … , 𝑝_𝑛) = ∑_{𝑇 ∈ 𝑇}_𝑠𝑡_𝑇(𝑝₁, … , 𝑝_𝑛) ∀ 𝑠 ∉ 𝑆_{𝑐𝑜𝑛𝑐} (2a) 𝑡𝑠(𝑝₁, … , 𝑝𝑛) = max

𝑇 ∈ 𝑇_𝑠𝑡𝑇(𝑝₁, … , 𝑝𝑛) ∀ 𝑠 ∈ 𝑆_{𝑐𝑜𝑛𝑐} (2b) 𝑆𝑠𝑒𝑞 is the set of all steps 𝑠 with sequential subtasks (see Table 2).

Due to the multi-grip performed by the system, the total unloading time for a given container is unloading time 𝑡 of an individual row times the total number of rows in a container. Assuming homogeneously stacked cartons, the number of rows in a container is the number of layers 𝑛_𝑥 in length times the number of layers 𝑛_𝑧 in height. Therefore, the total unloading time 𝑡 is

𝑡 = 𝑛𝑥 𝑛𝑧 𝑡𝑟 (3)

3.2 Parameter Identification

In a first step, we identified the external parameters of influence for each task by interviewing system and process experts. For each task, we deducted their elementary parameters from first- order principles and the expert evaluations. Thus, we can vary only task-relevant parameters, drastically reducing the number of tests necessary for each task. By combining the individual times with parameters of the unloading process (e.g., carton size), we can estimate the overall unloading time for varying conditions. Table 3 shows the relevant parameters affecting unloading performance and robustness for each task.

𝑇 Task Performance factors

1 Carton Detection object size (carton width, carton height), lightning (brightness, contrast), refractions, reflection (carton surface)

2 Chassis motion distance (carton depth), resistance (floor inclination) 3 Platform Motion distance (carton height), resistance (carton mass) 4 Gripper Motion resistance (carton mass, platform inclination)

5 Control Vacuum resistance (carton surface: porosity, carton mass, platform inclination)

6 Control Conveyor distance, resistance (carton mass)

Table 3. Parameters affecting unloading performance and robustness of tasks 𝑇

In the second step, we designed individual tests to analyze the effect of the corresponding parameters. For each parameter, we created discrete variations described in Table 4.

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𝑝 Parameter Parameter value

1 Carton surface matte; laminated

2 Brightness no ambient lighting; bright ambient lightning 3 Contrast equally distributed light; mixed lighting (spots)

4 Atmosphere no fog/dust (clean laboratory); Reduced visibility (fog) 5 Floor inclination -4%; 4%

6 Platform inclination 0%; 40%

7 Carton width 200 mm; 800 mm 8 Carton depth 200 mm; 800 mm 9 Carton height 200 mm; 800 mm 10 Carton mass 0 kg; 35 kg

Table 4. Range of values for the parameters

4 Experiments and results

For this contribution, we identified the times of the tasks that significantly affect the unloading time (tasks 2, 3, 4, 6). Each task was performed ten times for each combination of parameters.

Since all controllers are velocity controller, we only changed the parameters affecting the distance. The task time for the conveyor motion was determined on theoretical grounds. The total distance of 3.9 𝑚 can be covered in 5.6 𝑠 assuming a conveyor speed of 0.7 ^𝑚_𝑠. We performed all tests in a laboratory test-bed with a container of cartons of different sizes.

4.1 Preliminary Task Times

Table 5 lists the results of the experiments performed. It presents the mean time and its devi- ation for the slowest and fastest combination of parameters for each task. The total unloading time is given by the sum of all steps 𝑠 of the unloading process (Eq. 1). The time 𝑡_𝑠 of each step is defined by the total time or maximum time of all tasks 𝑇 ∈ 𝑠 as given in Table 2 (Eq. 2). Table 5 gives the minimal and maximal time of step task.

𝑇 Task 𝑡̅𝑇,𝑚𝑖𝑛 in s 𝑡̅𝑇,𝑚𝑎𝑥 in s

2 Chassis motion 4.0 12.0

3 Platform Motion 4.9 13.3

4 Gripper Motion 6.4 6.4

6 Control Conveyor 5.6 5.6

Table 5. Preliminary results of the unloading task times. The time for the conveyor motion was determined theoretically

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4.2 Performance Evaluation

The unloading performance is defined as the number of cartons per time (Table 1). The number of cartons per container depends on the size of the cartons. The size of conventional cartons are between 300×200×100 mm (small) and 800×640×600 mm (large). Therefore, the maximum number of cartons in a 1AA 40-feet container¹ is 9,009 with the long side oriented to the back.

The maximum number of large cartons is 126 also with the long side oriented to the back.

Table 6 presents the different scenarios and the stacking pattern for these scenarios and the estimated unloading performance. The total unloading time is calculated with Eq. (3). It should be noted that chassis and platform motion are performed concurrently and the gripper motion is performed twice, both when gripping and unloading.

Carton

size Number of cartons in length 𝑛_𝑥

Number of cartons in width 𝑛_𝑦

Number of cartons in height 𝑛_𝑧

Total number of cartons

Unloading time 𝑡_𝑟𝑜𝑤 per row in s

Unloading performance in carton/h

Small 39 21 11 9,009 23.2 3,252

Large 14 3 3 126 31.7 341

Table 6. Carton pattern for standard sized containers and the preliminary unloading performance

4.3 Limitations

The aforementioned test-setup allows for a flexible test of multiple criteria with a reduced over- head due to the evaluation of individual tasks. With equations (1–3), the final unloading time can be evaluated for a wide range of scenarios. This flexibility comes at the cost of lacking full- service evaluations. The estimated times are the result of distinct tests and present only an expected value for the total unloading time under various conditions. Since we did not perform full factorial tests and so far only tested for parcel size and mass, potential correlation between the parameters might affect the unloading performance. Additionally, the conveyor time was estimated based on conveyor velocity.

Additional effects will be evaluated in a field-test. There, actual 40ft-containers in the receiving area of a large logistics service provider will serve as the testbed for the system. With this test, we will evaluate the robustness over longer periods of time as well as the systems approach to unforeseen situations.

5. Summary

This paper presents a list of available solutions and their performance as well as a first approach towards a standardized evaluation of unloading throughput for automatic unloading solutions.

In the presented method for throughput estimation, the process is divided into multiple steps and the tasks in each steps are evaluated in terms of performance and robustness. We propose

1 internal dimensions of 11.998 × 2.330 × 2.350 m (International Organization for Standardization 2020)

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distinct tests for each task under variation of all parameters affecting performance and robustness and evaluate this division on a new unloading system, resulting in a setup with five different tasks and ten parameters.

In a first test, we estimate the unloading performance of the system to range from 341 cartons per hour for very large items to over 3,200 cartons per hour for small items. Next, we will evaluate all parameters affecting robustness in a laboratory environment. In field-tests we will evaluate the robustness and performance of the system under varying conditions.

Acknowledgments

This work was supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) under Grant 19H17016C.

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Planning and Optimization Of Internal Transport Systems

Uwe Wenzel, Managing Director, LOGSOL GmbH and Vice Chairperson of BVL chapter Saxony, Dresden

Franziska Pohl, Senior Product Manager, LOGSOL GmbH, Dresden

Maximilian Dörnbrack, Professional Product Manager, LOGSOL GmbH, Dresden Valeska Lippert, Student Employee Logistics Software, LOGSOL GmbH, Dresden

Summary. The digital transformation is increasingly affecting the field of intralogistics. In order to develop corresponding concepts in logistics planning in a short time, software tools should profitably transfer the theoretical knowledge into practice and support the user in the best possible way. This article gives insight in particular into the typical tasks involved in the planning and optimization of internal transport processes and how LOGSOL addresses these with the help of the RoutMan planning tool.

1. Introduction

Jeff Bezos, founder of the online sales company Amazon, described the current digital transformation of society with the emphatic phrase “There is no alternative to digital transformation” – transformation which now seems to us to be not only fundamental but also irreversible. In this context, the fourth industrial revolution encompasses all those structural changes that occur in the course of digital transformation in logistics and production (ten Hompel and Henke 2020).

And indeed, it is hardly surprising that logistics – with its algorithmic and deterministic nature – is one of the earliest areas of application for new technologies such as artificial intelligence or the Internet of Things (ten Hompel and Henke 2020). At the same time, it represents the link between companies in the value creation network and the interface between internal company functions, and thus proves to be a predestined playing field for continuous improvement (Hofmann and Nothardt 2009). Within this overall supply chain, intralogistics represents only one logistics task, but its role – as the process stage that determines the type and timing of material supply – can certainly be described as central (Miebach and Müller 2006). It, too, is subject to constant optimization initiatives. In this context and due to the fact that they are often still manually operated, internal floor-based transport systems in particular have a high potential for improvement (Wehking 2020).

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Wenzel, Pohl et al.

Lean design in intralogistics has been a primary objective of logistics planning at least since the worldwide establishment of the Toyota Production System. The challenge here is not only to realize high quality and efficiency with low lead times, but also to simultaneously optimize space utilization, personnel deployment, and inventory reduction (Liebetruth and Merkl, 2018). Even today, digital solutions such as automated guided vehicles, indoor tracking, E-KANBAN and many more already offer versatile possibilities for optimally exploiting the potential of internal transport concepts and their control. LOGSOL encounters such developments daily in the practical environment of logistics planning. In order to contribute to the growth of digitalization and the ongoing need for optimization in the planning of internal transport systems, LOGSOL developed a software-aided planning tool in the last few years.¹ In this context, LOGSOL also cooperates with scientific facilities and institutes, such as the Chair of Material Handling at the Technical University of Dresden.

2. Planning of internal transport processes

Planning internal transport processes is undoubtedly an extensive undertaking that depends on many factors. Although every project in this context is individual, established planning methods can be applied in almost every case and the complexity of the planning task can be reduced by a standardized procedure. The first stage of the planning process usually begins with the identification of potential transport concepts.

Over the past 30 years, both the technologies used in in-plant transportation and the associated processes have changed. In the course of time, highly complex and mostly partially digitalized conveyor systems have been created, the dimensioning and optimization of which requires considerable effort. A wide variety of transport concepts are available. Most widespread and relevant in terms of planning is the classification of these according to their underlying application concept. The most elementary distinction should be made between continuous conveyors – used to create a continuous transport flow through stationary line connections – and discontinuous conveyors – used to create an interrupted material flow (Jünemann and Schmidt 2000). The latter are used in particular for direct supply of materials to production (Wehking 2020).

Floor-bound non-continuous conveyors are characterized not only by the intense planning and control efforts associated with them. They also represent the group of conveyor technology that has experienced massive automation in recent years. The most common technologies include tugger trains, forklifts and automated guided vehicles (AGVs). Although they all come under the same classification, these conveyor systems differ from each other in many ways. Among the critical criteria to consider for their planning are (Wehking 2020):

 Flexibility in the event of changes to the layout, infrastructure or material flows

 Technical parameters, such as the conveying direction, load capacity and the turning radius

 Degree of automation as a factor influencing personnel costs and controllability

 Interactions among themselves and with each other and with adjacent processes

 Control effort

 Investment requirement

1 The planning and control of internal transport systems are closely interlinked. However, in practice, the focus is usually on digitalization in operational areas and not in planning (i.e., the design and dimensioning of flexible internal transport processes).

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Planning and Optimization of Internal Transport Systems

The selection of a suitable transport concept depends on a large number of contributing factors.

One of these factors is the preexisting, internal company requirements for the transport system being planned. Three planning paradigms play a decisive role here, regardless of the conveyor technology (see following figure).

Figure 1. Planning paradigms

The spatial dimension includes conditions within which transport processes are to be conceptualized. They refer to all storage, picking, transport and handling arrangements that constitute the framework for the transport of goods. The first step of their analysis starts with the visual recording and graphic mapping of abstract, mostly geometric basic structures of the company facilities (Martin 2016). Based on these planning fundamentals, initial considerations can then be made regarding the design of the transport system. Relevant here is the definition of sources – delivery points where materials are made available – and sinks – receiving points where materials are required (Liebetruth and Merkl 2018). The transport task to be performed, such as production supply, disposal, or transport between storage points, determines both the number and type of sources and sinks. With the help of this spatial visualization, organizational principles can be determined in the next step. If sinks are in a linear arrangement, flow production can be assumed. Much higher planning efforts result from a station or island-like arrangement (Lieb et al. 2017). In any case, the analysis of spatial dimensioning provides an adequate first point of reference for possible restrictions and thus for the delimitation of potential transport concept right from the beginning of planning.

Material dimensioning is mainly about the goods to be transported and their characteristics. In this context, the generic dimensions should be addressed in more detail. The assessment of these qualitative properties of the material to be transported is essential in order to narrow down the applicable transport concepts, due to the requirements of the material for the transport process (Martin 2016). Quality standards for the materials to be transported also play a decisive role. For example, the planner is confronted with questions regarding the additional effort required for unpacking, packing or creating sets (Liebetruth and Merkl 2018). Aside from this, the quantitative dimensions also play a decisive role in material dimensioning (Martin 2016).

No less essential for the planning of the transport system is time dimensioning. This planning paradigm is a frequent reason for exclusion of unsuitable transport concepts, especially for transport assignments supplying production. The most important aspects include operating speed, cycle time, and replenishment time (Liebetruth and Merkl 2018). Taking into account these planning requirements, conveyor technology can be evaluated and selected for speed and flexibility.

The process of recording and analyzing all of these planning fundamentals can be more or less complex, depending on the project conditions. In order to at least partially simplify and standardize it, planners draw on various supporting methods and tools.

The basis for all planning projects is initially the compilation of the numerical data. This includes the compilation of qualitative and quantitative information on the transportation process. These

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Wenzel, Pohl et al.

are typically composed of the planning paradigms already presented and rely on the availability of relevant data. Often these already exist in the company, but it is not uncommon to have to collect missing data during the planning process. Some methods and tools for transportation planning that have proven to be effective will be presented in the following.

The MTM method is often used in practice, especially when it comes to recording and analyzing time planning dimensions. Since the data situation for determining relevant time requirements does not always correspond to planning needs, the need for analog recording of working hours sometimes cannot be avoided. With the help of the MTM method, processes can be grouped into modules and evaluated independently of employees on the basis of statistically determined standard times (Liebetruth 2020).

Since planning a transportation system does not always involve a full-scale redesign, existing structures may need to be analyzed. The distance-intensity diagram supports the planner by classifying material flow relationships according to their intensity – i.e., transport demand – and the distance between source and sink (distance). They can be classified and prioritized according to their effort. Using a classic heat map, the traffic intensity can also be visualized. The planner can see the line load distribution and optimization potential at a glance.

3. LOGSOL’s approach to planning and optimizing internal transport processes

Over the past 20 years, LOGSOL has gained a wealth of experience in planning and optimizing transport processes. While there is still a relatively high degree of freedom and relatively few restrictions in new plant planning or expansion, the restructuring of an existing transport system presents a much greater challenge. Typical reasons for having to adapt planning are, for example, fluctuations in demand – triggered by internal or external factors – and structural changes, such as the conversion of a production facility from workshop to assembly line production or a change in the product manufacturing portfolio.

Another reason for the need to plan a transport system, which is already relevant today and will remain so in the future, is the increasing digitalization of logistics processes. The introduction of new conveyor technologies that are able to collect multidimensional data, communicate or operate fully automatically is now one of the most important reasons for replacing or restructuring existing transport processes. In practice, there is a considerable amount of catching up to do here, as the focus is usually on control and less on planning, which generally provides the guidelines for operational management.

Depending on the motivations for planning a transportation system and the information available on the company’s internal planning paradigms, the level of effort required may vary. A decisive factor here is hidden in the planning and optimization process itself. A variety of data-driven analysis tools now exist to make the planning process more efficient. However, these usually come from different providers and involve significant integration effort. To overcome this barrier in its own planning projects and for its customers, LOGSOL developed a software-based application for comprehensive planning and optimization of internal transport processes, as part of a research and development project.

As already shown, the planning of a transport system requires four basic pieces of information relevant to design, whereby the technical planning paradigm refers to the conveyor technology itself and the spatial, material and time dimensions represent the company’s existing structural characteristics. In the first step in software-aided planning, data on the relevant means of

International Scientific Symposium on Logistics 2021

2021

International Scientific Symposium on Logistics

June 15, 2021

Conference Volume

Digital Event

2021

International Scientific Symposium on Logistics

Conference Volume

edited by Thorsten Schmidt

Kai Furmans Michael Freitag Bernd Hellingrath

René de Koster

Anne Lange

Contents

Preface of the Editors

Preface of Bundesvereinigung Logistik (BVL)

Solving Sustainability Problems:

Lessons Learned in Transport and Logistics

Behavioral Issues in Automated Warehouses:

Unifying Framework and Research Agenda

1. Introduction and Motivation

2. Research Methodology

3. Unifying Framework for Human-Machine Interactions in Warehouses

4. Relevant Behavioral Issues of Human-Machine Interactions

5. Conclusion

References

Method for the Evaluation of

An Autonomous Handling System

For Improving the Process Efficiency of Container Unloading

1. Introduction

2. State of the Art and System Description

3. Method

4 Experiments and results

5. Summary

Acknowledgments

References

Planning and Optimization Of Internal Transport Systems

1. Introduction

2. Planning of internal transport processes

3. LOGSOL’s approach to planning and optimizing internal transport processes